Algorithmic Cluster Expansions for Quantum Problems

We establish a general framework for developing approximation algorithms for a class of counting problems. Our framework is based on the cluster expansion of the abstract polymer model formalism of Kotecký and Preiss. We apply our framework to obtain efficient algorithms for (1) approximating probab...

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Main Authors: Ryan L. Mann, Romy M. Minko
Format: Article
Language:English
Published: American Physical Society 2024-01-01
Series:PRX Quantum
Online Access:http://doi.org/10.1103/PRXQuantum.5.010305
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author Ryan L. Mann
Romy M. Minko
author_facet Ryan L. Mann
Romy M. Minko
author_sort Ryan L. Mann
collection DOAJ
description We establish a general framework for developing approximation algorithms for a class of counting problems. Our framework is based on the cluster expansion of the abstract polymer model formalism of Kotecký and Preiss. We apply our framework to obtain efficient algorithms for (1) approximating probability amplitudes of a class of quantum circuits close to the identity, (2) approximating expectation values of a class of quantum circuits with operators close to the identity, (3) approximating partition functions of a class of quantum spin systems at high temperature, and (4) approximating thermal expectation values of a class of quantum spin systems at high temperature with positive-semidefinite operators. Further, we obtain hardness of approximation results for approximating probability amplitudes of quantum circuits and partition functions of quantum spin systems. This establishes a computational complexity transition for these problems and shows that our algorithmic conditions are optimal under complexity-theoretic assumptions. Finally, we show that our algorithmic condition is almost optimal for expectation values and optimal for thermal expectation values in the sense of zero freeness.
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spelling doaj.art-2dd171b696a24583a5f6dca4a527f5ec2024-01-16T15:49:36ZengAmerican Physical SocietyPRX Quantum2691-33992024-01-015101030510.1103/PRXQuantum.5.010305Algorithmic Cluster Expansions for Quantum ProblemsRyan L. MannRomy M. MinkoWe establish a general framework for developing approximation algorithms for a class of counting problems. Our framework is based on the cluster expansion of the abstract polymer model formalism of Kotecký and Preiss. We apply our framework to obtain efficient algorithms for (1) approximating probability amplitudes of a class of quantum circuits close to the identity, (2) approximating expectation values of a class of quantum circuits with operators close to the identity, (3) approximating partition functions of a class of quantum spin systems at high temperature, and (4) approximating thermal expectation values of a class of quantum spin systems at high temperature with positive-semidefinite operators. Further, we obtain hardness of approximation results for approximating probability amplitudes of quantum circuits and partition functions of quantum spin systems. This establishes a computational complexity transition for these problems and shows that our algorithmic conditions are optimal under complexity-theoretic assumptions. Finally, we show that our algorithmic condition is almost optimal for expectation values and optimal for thermal expectation values in the sense of zero freeness.http://doi.org/10.1103/PRXQuantum.5.010305
spellingShingle Ryan L. Mann
Romy M. Minko
Algorithmic Cluster Expansions for Quantum Problems
PRX Quantum
title Algorithmic Cluster Expansions for Quantum Problems
title_full Algorithmic Cluster Expansions for Quantum Problems
title_fullStr Algorithmic Cluster Expansions for Quantum Problems
title_full_unstemmed Algorithmic Cluster Expansions for Quantum Problems
title_short Algorithmic Cluster Expansions for Quantum Problems
title_sort algorithmic cluster expansions for quantum problems
url http://doi.org/10.1103/PRXQuantum.5.010305
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AT romymminko algorithmicclusterexpansionsforquantumproblems